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Cloud and Big Data in Finance: How It Helps

September 16, 2017

Cloud computing and big data go hand in hand. The more complex the big data analyses, the more data storage and computing power are required. And perhaps no other field has more complex computing models than financial services.

Big data has enabled greater accuracy in financial performance forecasting, risk assessment, customer portfolio management and more. Among other abilities, it can bring together data from multiple sources, such as market variables and historical performance, to perform accurate “what if” scenarios. That’s why cloud computing and big data have become essential tools in finance, and more financial services companies are looking for savvy solution providers to help them manage cloud computing and expand their big data strategies.

IDC Financial Insights estimates that out of total IT spending of $455 billion, the financial services industry has spent more than $114 billion worldwide on mobile services, cloud and big data technology. IDC analysts also estimate that spending in these three categories will climb from 25 percent to 30 percent by 2019.

Financial institutions are transforming their operations with mobility, big data and cloud computing. Emerging technologies allow banks and investment firms to learn more about their customers as well as track changes in the market, and they can use that data for analytical modeling to make faster, more accurate transactions and business decisions.

How Finance Is Harnessing Big Data


Like other industries, financial services firms are using big data to improve various aspects of their operations. Here are just a few applications for big data specific to the financial market:

Algorithmic trading 
Computer technology has made it possible to execute financial trades faster than a human trader can react. Using mathematical formulas powered by big data analytics, trades can be executed quickly using preset prices and closely timed trading. Algorithmic trading also reduces the amount of human error in trades.

Traders also can use historical data to test investment strategies and reduce risk. With big data analytics, they also can incorporate external data that affects the market, such as real-time news, social media trending and stock market changes. This kind of “robo-trading” eliminates the human element and makes decisions based strictly on available data.

Profiling customer behavior

Big data analytics are ideal for creating comprehensive customer profiles. In financial services, those profiles include long-term financial goals and custom risk profiles. Big data analytics provide a strategic value by creating predictive models that show how customers and competitors will behave and using that insight to tailor products and pricing. Using big data analytics, including external data sources such as social media, can improve customer interaction as well as portfolio performance.

For example, banks are using internal and external data sources, such as social media, voice, video and text, to develop personalized banking products. Analytics also are playing a larger role in personalized lending and bad debt recovery, making it possible to develop more accurate profiles that assess customers’  financial situation beyond their credit scores.

Data security and fraud detection

Big data is an ideal tool for automating fraud detection. Real-time analytics can be used to power models that generate near-real-time fraud alerts. Analytics can identify a data breach or suspicious activity and even generate a programmed response to stop a transaction or data transfer until it can be investigated.

There are countless other applications for big data analytics in financial services. All those analytics applications are being driven by the three Vs of big data—volume, velocity and variety.

Managing the Cloudburst of Data

Financial big data analytical models are using both structured and unstructured data, including internal financial records and historical data and external data such as stock market feeds, news and social media commentary. The more data that has to be assimilated, the more data storage and computing power are required, which is why more financial services companies are expanding their cloud strategy.

According to research from IBM and the Said Business School at the University of Oxford, most pilot programs for banks and financial services companies start with analyses of disparate types of archived data locked in their systems, such as transactions (reported by 92 percent), log data (81 percent), events (70 percent), email (65 percent) and social media (27 percent). Financial institutions now are broadening their analytics to encompass other data sources, such as call center transcripts to improve big data models. The ability to support the volume, variety and velocity of data makes cloud computing essential.

Data visualization is another challenge for financial companies. Traditional data mining and reporting strategies can’t deal with the large datasets, so more financial institutions are using cloud services to support sophisticated visualization.

In many ways, financial services is still an untapped market for big data and cloud computing. Banks, brokers and financial services firms have just started to effectively mine archived data and understand how to apply external data sources to improve analytical accuracy. They also have special considerations with regard to privacy and security, which means many firms are still cautious about cloud adoption. Solution providers can show them the way with big data services and private and hybrid cloud strategies that can help them harness the data they need for analytical modeling.